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A Task-Driven Code Stream Structured Image Coding Method

An image coding, task-driven technology, applied in the field of image coding and deep learning, can solve problems such as a large amount of transmission bandwidth and storage space, and achieve the effect of improving efficiency

Active Publication Date: 2020-08-28
UNIV OF SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The current methods all need to transmit, store and decode the compressed image or video binary stream data, restore it into an image and video signal, and then perform machine intelligent analysis. With the collection and processing of a large amount of image and video data, this will require a lot of Transmission bandwidth, storage space and decoding operation

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  • A Task-Driven Code Stream Structured Image Coding Method
  • A Task-Driven Code Stream Structured Image Coding Method
  • A Task-Driven Code Stream Structured Image Coding Method

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Embodiment Construction

[0029] Such as figure 1 As shown, a task-driven code stream structured image encoding method of the present invention is divided into two parts, including a code stream structured encoder and a decoder, and the code stream structured encoder realizes the structured representation and compression of the input image , the decoder reconstructs the input image according to the compressed features.

[0030] Code stream structured encoder, mainly including feature extraction, target detection, quantization, predictive coding and object-based code stream division process; feature extraction, multi-scale feature extraction and fusion of input images, and its output is used as quantization and target detection at the same time Input: the input image is subjected to down-sampling operations with different step lengths, so as to obtain the input image features captured under different receptive field conditions, transform these features to the same size and connect them together to obta...

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Abstract

The invention relates to a task-driven code stream structured image encoding method, comprising: a code stream structured encoder and a decoder. The feature reconstructs the input image; the code stream structured coder includes: feature extraction, target detection, quantization, predictive encoding process and object-based code stream division process; the present invention encodes the image, and in the process of encoding Objects are detected at the layer level, and structured code streams are generated based on the detection results, so as to achieve the purpose of selecting part of the structured code streams or all code streams for analysis according to different intelligent analysis tasks, making the process of image coding and transmission applications more efficient. for efficiency and flexibility.

Description

technical field [0001] The invention relates to a task-driven code stream structured image encoding method, which belongs to the technical field of image encoding and deep learning. Background technique [0002] The existing learning-based image compression methods are mostly optimized from the perspective of rate-distortion. However, with the gradual deepening and maturity of research work related to deep learning applications, image or video information will be used as input for machine intelligence analysis tasks in more and more occasions, such as surveillance video analysis, automatic driving, remote interaction, and telemedicine. etc. The current methods all need to transmit, store and decode the compressed image or video binary stream data, restore it into an image and video signal, and then perform machine intelligent analysis. With the collection and processing of a large amount of image and video data, this will require a lot of Transmission bandwidth, storage sp...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N19/124H04N19/147H04N19/42H04N19/44H04N19/50H04N19/70H04N19/91G06N3/04
CPCH04N19/124H04N19/42H04N19/44H04N19/50H04N19/70H04N19/91H04N19/147G06N3/045
Inventor 陈志波何天宇孙思萌
Owner UNIV OF SCI & TECH OF CHINA